class_docstr: Importance-weighted variational inference.
  
  Uses importance sampling to produce an improved lower bound on the
  log marginal likelihood. It is the core idea behind
  importance-weighted autoencoders (Burda et al. (2016)).
class_docstr_tok: ["Importance-weighted","variational","inference",".","Uses","importance","sampling","to","produce","an","improved","lower","bound","on","the","log","marginal","likelihood",".","It","is","the","core","idea","behind","importance-weighted","autoencoders","(","Burda","et","al",".","(","2016",")",")","."]
code: def __init__(self, *args, **kwargs):
      super(IWVI, self).__init__(*args, **kwargs)
  
file_path: edward/examples/iwvi.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import six
  import tensorflow
  from edward.inferences import VariationalInference
  from edward.models import Bernoulli, Normal, RandomVariable
  from edward.util import copy
  from scipy.special import expit
label_desc: Use image node_id 1 to create a new IWVI object from inherited base classes: VariationalInference with example: obj = IWVI()